Tutorial and spreadsheet for the evaluation of instrumental quantification uncertainty by the linear weighted regression model: Determination of elemental impurities in a nasal spray by ICP-MS

Weighted Linear Regression Improves Accuracy of Quantitative Elemental Bioimaging by Means of LA-ICP-MS

The application of ordinary least squares (OLS) linear regression is widely used in order to approximate linear external calibration data. However, the assumption of homoscedasticity is often not considered as a requirement for correct data approximation, which can result in a poor regression fit that is often more prominent in the lower concentration range. Heteroscedasticity in inductively coupled plasma-mass spectrometry (ICP-MS) data has been discussed in literature as an intrinsic problem and was found to be addressed better by the use of weighted least squares (WLS) regression in multiple studies. In this study, the effects of OLS and WLS linear regression models have been investigated for quantitative imaging experiments by means of laser ablation (LA)-ICP-MS using matrix-matched standards. The calibration data produced by this technique was found to be heteroscedastic in all 60 analyzed datasets, which yielded poor regression fits for OLS linear regression. In comparison to conventional ICP-MS analysis, the resulting negative effects were found to become even more visible in imaging LA-ICP-MS due to an inaccurate estimation of the regression line's intercept. Also, the calculation of average concentrations in selected regions of interest (ROIs) yields incorrect quantification results at the lower end of the calibration range. The application of WLS linear regression resulted in an improved goodness of fit (GOF), although the weighting factor should be selected carefully. Besides the reciprocal of the variance of each calibration standard (1/s_i²), more empirical weighting factors that have been discussed in the literature were also evaluated regarding the GOF.

Evaluation and validation of detailed and simplified models of the uncertainty of unified [Formula: see text] measurements in aqueous solutions

The use of the unified pH concept, [Formula: see text] , applicable to aqueous and non-aqueous solutions, which allows interpreting and comparison of the acidity of different types of solutions, requires reliable and objective determination. The [Formula: see text] can be determined by a single differential potentiometry measurement referenced to an aqueous reference buffer or by a ladder of differential potentiometric measurements that allows minimisation of inconsistencies of various determinations. This work describes and assesses bottom-up evaluations of the uncertainty of these measurements, where uncertainty components are combined by the Monte Carlo Method (MCM) or Taylor Series Approximation (TSM). The MCM allows a detailed simulation of the measurements, including an iterative process involving in minimising ladder deviations. On the other hand, the TSM requires the approximate determination of minimisation uncertainty. The uncertainty evaluation was successfully applied to measuring aqueous buffers with pH of 2.00, 4.00, 7.00, and 10.00, with a standard uncertainty of 0.01. The reference and estimated values from both approaches are metrologically compatible for a 95% confidence level even when a negligible contribution of liquid junction potential uncertainty is assumed. The MCM estimated pH values with an expanded uncertainty, for the 95% confidence level, between 0.26 and 0.51, depending on the pH value and ladder inconsistencies. The minimisation uncertainty is negligible or responsible for up to 87% of the measurement uncertainty. The TSM quantified measurement uncertainties on average only 0.05 units larger than the MCM estimated ones. Additional experimental tests should be performed to test these uncertainty models for analysis performed in other laboratories and on non-aqueous solutions.

Measurement Uncertainty and Conformity Assessment Applied to Drug and Medicine Analyses - A Review

Analytical results are often used in scientific research, industrial and clinical applications to support decision making. Despite all efforts to ensure the reliability of analytical results (including method validation, internal quality control, use of certified reference materials, proficiency tests, and ISO 17025 accreditation), there will always be an uncertainty associated with the measured value. The measurement uncertainty expresses the quality of the analytical result and allows the comparability between analytical results or between the measured value and the specification limit(s). This work discusses the importance of measurement uncertainty, including the steps involved in the measurement uncertainty evaluation, the bottom-up and top-down approaches used in measurement uncertainty calculation, the measurement uncertainty evaluation in drug and medicine analyses, and the application of measurement uncertainty in conformity assessment for quality control, stability studies, and pharmaceutical equivalence.